mcts-catherineannie13 0.0.1

A package to run MCTS for Monopoly

Monopoly MCTS AI

Monopoly MCTS AI is a Python package that implements the Monte Carlo Tree Search (MCTS) algorithm for decision-making in simulated Monopoly games. It utilizes neural networks for strategy optimization and state evaluation, providing a framework for researching AI-driven decision-making in board games.

Features

• Implementation of the Monte Carlo Tree Search (MCTS) algorithm.
• Use of neural networks (NN) for evaluating game states and making decisions.
• Simulation of Monopoly game dynamics including properties, stations, utilities, and player interactions.
• Customizable strategies for AI players.

Installation

Option 1

Clone this repository to your local machine:

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git clone https://github.com/catherineannie13/Capstone-Optimising-Monopoly-Gameplay-Strategies.git
cd monopoly-mcts-ai

Ensure you have Python 3.8 or later installed. It's recommended to use a virtual environment:

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python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install the required dependencies:

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pip install -r requirements.txt

Option 2

Run pip install -i https://pypi.org/simple/ mcts-catherineannie13==0.0.1

Usage

To run a simulation of the Monopoly game with the MCTS AI, execute the following command in the root directory of the project:

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python -m MonopolyBoardMCTS

You can customize the simulation parameters within the MonopolyBoardMCTS.py script or by modifying the command-line interface (if implemented) to adjust the number of games, AI strategies, and other settings.

Contributing

Contributions to the Monopoly MCTS AI project are welcome. Please follow the standard fork-and-pull request workflow on GitHub. Ensure you write or update tests as necessary.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• This project is inspired by the classic board game Monopoly.
• Monte Carlo Tree Search algorithm for strategic decision-making.
• Neural network implementation for state evaluation.